Results 451-500 of 2056 (2027 ASCL, 29 submitted)

[ascl:1102.024]
DiFX2: A more flexible, efficient, robust and powerful software correlator

Deller, A. T.; Brisken, W. F.; Phillips, C. J.; Morgan, J.; Alef, W.; Cappallo, R.; Middelberg, E.; Romney, J.; Rottmann, H.; Tingay, S. J.; Wayth, R.

Software correlation, where a correlation algorithm written in a high-level language such as C++ is run on commodity computer hardware, has become increasingly attractive for small to medium sized and/or bandwidth constrained radio interferometers. In particular, many long baseline arrays (which typically have fewer than 20 elements and are restricted in observing bandwidth by costly recording hardware and media) have utilized software correlators for rapid, cost-effective correlator upgrades to allow compatibility with new, wider bandwidth recording systems and improve correlator flexibility. The DiFX correlator, made publicly available in 2007, has been a popular choice in such upgrades and is now used for production correlation by a number of observatories and research groups worldwide. Here we describe the evolution in the capabilities of the DiFX correlator over the past three years, including a number of new capabilities, substantial performance improvements, and a large amount of supporting infrastructure to ease use of the code. New capabilities include the ability to correlate a large number of phase centers in a single correlation pass, the extraction of phase calibration tones, correlation of disparate but overlapping sub-bands, the production of rapidly sampled filterbank and kurtosis data at minimal cost, and many more. The latest version of the code is at least 15% faster than the original, and in certain situations many times this value. Finally, we also present detailed test results validating the correctness of the new code.

[ascl:1904.023]
digest2: NEO binary classifier

Keys, Sonia; Vereš, Peter; Payne, Matthew J.; Holman, Matthew J.; Jedicke, Robert; Williams, Gareth V.; Spahr, Tim; Asher, David J.; Hergenrother, Carl

digest2 classifies Near-Earth Object (NEO) candidates by providing a score, *D _{2}*, that represents a pseudo-probability that a tracklet belongs to a given solar system orbit type. The code accurately and precisely distinguishes NEOs from non-NEOs, thus helping to identify those to be prioritized for follow-up observation. This fast, short-arc orbit classifier for small solar system bodies code is built upon the Pangloss code developed by Robert McNaught and further developed by Carl Hergenrother and Tim Spahr and Robert Jedicke's 223.f code.

[ascl:1010.031]
DimReduce: Nonlinear Dimensionality Reduction of Very Large Datasets with Locally Linear Embedding (LLE) and its Variants

DimReduce is a C++ package for performing nonlinear dimensionality reduction of very large datasets with Locally Linear Embedding (LLE) and its variants. DimReduce is built for speed, using the optimized linear algebra packages BLAS, LAPACK, and ARPACK. Because of the need for storing very large matrices (1000 by 10000, for our SDSS LLE work), DimReduce is designed to use binary FITS files as inputs and outputs. This means that using the code is a bit more cumbersome. For smaller-scale LLE, where speed of computation is not as much of an issue, the Modular Data Processing toolkit may be a better choice. It is a python toolkit with some LLE functionality, which VanderPlas contributed.

This code has been rewritten and included in scikit-learn and an improved version is included in http://mmp2.github.io/megaman/

[ascl:1908.005]
dips: Detrending periodic signals in timeseries

dips detrends timeseries of strictly periodic signals. It does not assume any functional form for the signal or the background or the noise; it disentangles the strictly periodic component from everything else. It has been used for detrending Kepler, K2 and TESS timeseries of periodic variable stars, eclipsing binary stars, and exoplanets.

[ascl:1405.016]
DIPSO: Spectrum analysis code

DIPSO plots spectroscopic data rapidly and combines analysis and high-quality graphical output in a simple command-line driven interactive environment. It can be used, for example, to fit emission lines, measure equivalent widths and fluxes, do Fourier analysis, and fit models to spectra. A macro facility allows convenient execution of regularly used sequences of commands, and a simple Fortran interface permits "personal" software to be integrated with the program. DIPSO is part of the Starlink software collection (ascl:1110.012).

[ascl:1806.015]
DirectDM-mma: Dark matter direct detection

The Mathematica code DirectDM takes the Wilson coefficients of relativistic operators that couple DM to the SM quarks, leptons, and gauge bosons and matches them onto a non-relativistic Galilean invariant EFT in order to calculate the direct detection scattering rates. A Python implementation of DirectDM is also available (ascl:1806.016).

[ascl:1806.016]
DirectDM-py: Dark matter direct detection

DirectDM, written in Python, takes the Wilson coefficients of relativistic operators that couple DM to the SM quarks, leptons, and gauge bosons and matches them onto a non-relativistic Galilean invariant EFT in order to calculate the direct detection scattering rates. A Mathematica implementation of DirectDM is also available (ascl:1806.015).

[ascl:1102.021]
DIRT: Dust InfraRed Toolbox

DIRT is a Java applet for modelling astrophysical processes in circumstellar dust shells around young and evolved stars. With DIRT, you can select and display over 500,000 pre-run model spectral energy distributions (SEDs), find the best-fit model to your data set, and account for beam size in model fitting. DIRT also allows you to manipulate data and models with an interactive viewer, display gas and dust density and temperature profiles, and display model intensity profiles at various wavelengths.

[ascl:1403.020]
disc2vel: Tangential and radial velocity components derivation

Disc2vel derives tangential and radial velocity components in the equatorial plane of a barred stellar disc from the observed line-of-sight velocity, assuming geometry of a thin disc. The code is written in IDL, and the method assumes that the bar is close to steady state (i.e. does not evolve fast) and that both morphology and kinematics are symmetrical with respect to the major axis of the bar.

[ascl:1605.011]
DISCO: 3-D moving-mesh magnetohydrodynamics package

DISCO evolves orbital fluid motion in two and three dimensions, especially at high Mach number, for studying astrophysical disks. The software uses a moving-mesh approach with a dynamic cylindrical mesh that can shear azimuthally to follow the orbital motion of the gas, thus removing diffusive advection errors and permitting longer timesteps than a static grid. DISCO uses an HLLD Riemann solver and a constrained transport scheme compatible with the mesh motion to implement magnetohydrodynamics.

[ascl:1209.011]
DiskFit: Modeling Asymmetries in Disk Galaxies

Kuzio de Naray, Rache; Arsenault, Cameron A.; Spekkens, Kristine; Sellwood, J. A.; McDonald, Michael; Simon, Joshua D.; Teuben, Peter

DiskFit implements procedures for fitting non-axisymmetries in either kinematic or photometric data. DiskFit can analyze H-alpha and CO velocity field data as well as HI kinematics to search for non-circular motions in the disk galaxies. DiskFit can also be used to constrain photometric models of the disc, bar and bulge. It deprecates an earlier version, by a subset of these authors, called velfit.

[ascl:1603.011]
DiskJockey: Protoplanetary disk modeling for dynamical mass derivation

DiskJockey derives dynamical masses for T Tauri stars using the Keplerian motion of their circumstellar disks, applied to radio interferometric data from the Atacama Large Millimeter Array (ALMA) and the Submillimeter Array (SMA). The package relies on RADMC-3D (ascl:1202.015) to perform the radiative transfer of the disk model. DiskJockey is designed to work in a parallel environment where the calculations for each frequency channel can be distributed to independent processors. Due to the computationally expensive nature of the radiative synthesis, fitting sizable datasets (e.g., SMA and ALMA) will require a substantial amount of CPU cores to explore a posterior distribution in a reasonable timeframe.

[ascl:1811.013]
DiskSim: Modeling Accretion Disk Dynamics with SPH

DiskSim is a source-code distribution of the SPH accretion disk modeling code previously released in a Windows executable form as FITDisk (ascl:1305.011). The code released now is the full research code in Fortran and can be modified as needed by the user.

[ascl:1108.015]
DISKSTRUCT: A Simple 1+1-D Disk Structure Code

DISKSTRUCT is a simple 1+1-D code for modeling protoplanetary disks. It is not based on multidimensional radiative transfer! Instead, a flaring-angle recipe is used to compute the irradiation of the disk, while the disk vertical structure at each cylindrical radius is computed in a 1-D fashion; the models computed with this code are therefore approximate. Moreover, this model cannot deal with the dust inner rim.

In spite of these simplifications and drawbacks, the code can still be very useful for disk studies, for the following reasons:

- It allows the disk structure to be studied in a 1-D vertical fashion (one radial cylinder at a time). For understanding the structure of disks, and also for using it as a basis of other models, this can be a great advantage.
- For very optically thick disks this code is likely to be much faster than the RADMC full disk model.
- Viscous internal heating of the disk is implemented and converges quickly, whereas the RADMC code is still having difficulty to deal with high optical depth combined with viscously generated internal heat.

[ascl:1708.006]
DISORT: DIScrete Ordinate Radiative Transfer

DISORT (DIScrete Ordinate Radiative Transfer) solves the problem of 1D scalar radiative transfer in a single optical medium, such as a planetary atmosphere. The code correctly accounts for multiple scattering by an isotropic or plane-parallel beam source, internal Planck sources, and reflection from a lower boundary. Provided that polarization effects can be neglected, DISORT efficiently calculates accurate fluxes and intensities at any user-specified angle and location within the user-specified medium.

[ascl:1302.015]
DisPerSE: Discrete Persistent Structures Extractor

DisPerSE is open source software for the identification of persistent topological features such as peaks, voids, walls and in particular filamentary structures within noisy sampled distributions in 2D, 3D. Using DisPerSE, structure identification can be achieved through the computation of the discrete Morse-Smale complex. The software can deal directly with noisy datasets via the concept of persistence (a measure of the robustness of topological features). Although developed for the study of the properties of filamentary structures in the cosmic web of galaxy distribution over large scales in the Universe, the present version is quite versatile and should be useful for any application where a robust structure identification is required, such as for segmentation or for studying the topology of sampled functions (for example, computing persistent Betti numbers). Currently, it can be applied can work indifferently on many kinds of cell complex (such as structured and unstructured grids, 2D manifolds embedded within a 3D space, discrete point samples using delaunay tesselation, and Healpix tesselations of the sphere). The only constraint is that the distribution must be defined over a manifold, possibly with boundaries.

[ascl:1812.012]
distlink: Minimum orbital intersection distance (MOID) computation library

distlink computes the minimum orbital intersection distance (MOID), or global minimum of the distance between the points lying on two Keplerian ellipses by finding all stationary points of the distance function, based on solving an algebraic polynomial equation of 16th degree. The program tracks numerical errors and carefully treats nearly degenerate cases, including practical cases with almost circular and almost coplanar orbits. Benchmarks confirm its high numeric reliability and accuracy, and even with its error-controlling overheads, this algorithm is a fast MOID computation method that may be useful in processing large catalogs. Written in C++, the library also includes auxiliary functions.

[submitted]
DM phase: A novel algorithm for correcting dispersion of radio signals

Radio waves propagating in space are subject to frequency-dependent delay due to interactions with cold free electrons, which gives coherent radio emissions a unique structure known as dispersion. The study of impulsive radio signals from astronomical sources, such as those emitted by pulsars and fast radio bursts (FRBs), requires proper corrections for this effect. Moreover, the ionized medium itself can be characterized by sensitive measurements of this dispersion.

Signal dispersion is proportional to the integrated column density of free electrons along the line of sight, a quantity known as dispersion measure (DM), and inversely proportional to the observing frequency squared. Traditional methods search for the best DM value of a source by maximizing the signal-to-noise ratio (S/N) of the detected signal. While sensitive and efficient algorithms have been designed for this purpose, they are affected by two limitations. Firstly, they implicitly assume a broadband emission across the entire observing frequency bandwidth. While this is normally true for pulsars, some FRBs have been observed to have complex spectra which returned incorrect DM values. Secondly, these traditional algorithms are highly sensitive to large-amplitude events such as large noise spikes and radio interference. In order to overcome these limitations, we developed a new algorithm to maximize the coherent power of the signal instead of its intensity. Since the structure of the signal is coherent at different frequencies, this method is relatively insensitive to complex spectro-temporal shapes of the pulses. In addition, this method is more robust to noise and interference because these normally have incoherent structures and the amplitude information in each frequency channel is discarded.

[ascl:1705.002]
DMATIS: Dark Matter ATtenuation Importance Sampling

DMATIS (Dark Matter ATtenuation Importance Sampling) calculates the trajectories of DM particles that propagate in the Earth's crust and the lead shield to reach the DAMIC detector using an importance sampling Monte-Carlo simulation. A detailed Monte-Carlo simulation avoids the deficiencies of the SGED/KS method that uses a mean energy loss description to calculate the lower bound on the DM-proton cross section. The code implementing the importance sampling technique makes the brute-force Monte-Carlo simulation of moderately strongly interacting DM with nucleons computationally feasible. DMATIS is written in Python 3 and MATHEMATICA.

[ascl:1506.002]
dmdd: Dark matter direct detection

The dmdd package enables simple simulation and Bayesian posterior analysis of recoil-event data from dark-matter direct-detection experiments under a wide variety of scattering theories. It enables calculation of the nuclear-recoil rates for a wide range of non-relativistic and relativistic scattering operators, including non-standard momentum-, velocity-, and spin-dependent rates. It also accounts for the correct nuclear response functions for each scattering operator and takes into account the natural abundances of isotopes for a variety of experimental target elements.

[ascl:1010.029]
DNEST: Diffusive Nested Sampling

This code is a general Monte Carlo method based on Nested Sampling (NS) for sampling complex probability distributions and estimating the normalising constant. The method uses one or more particles, which explore a mixture of nested probability distributions, each successive distribution occupying ~e^-1 times the enclosed prior mass of the previous distribution. While NS technically requires independent generation of particles, Markov Chain Monte Carlo (MCMC) exploration fits naturally into this technique. This method can achieve four times the accuracy of classic MCMC-based Nested Sampling, for the same computational effort; equivalent to a factor of 16 speedup. An additional benefit is that more samples and a more accurate evidence value can be obtained simply by continuing the run for longer, as in standard MCMC.

[ascl:1604.007]
DNest3: Diffusive Nested Sampling

DNest3 is a C++ implementation of Diffusive Nested Sampling (ascl:1010.029), a Markov Chain Monte Carlo (MCMC) algorithm for Bayesian Inference and Statistical Mechanics. Relative to older DNest versions, DNest3 has improved performance (in terms of the sampling overhead, likelihood evaluations still dominate in general) and is cleaner code: implementing new models should be easier than it was before. In addition, DNest3 is multi-threaded, so one can run multiple MCMC walkers at the same time, and the results will be combined together.

[ascl:1608.013]
DOLPHOT: Stellar photometry

DOLPHOT is a stellar photometry package that was adapted from HSTphot for general use. It supports two modes; the first is a generic PSF-fitting package, which uses analytic PSF models and can be used for any camera. The second mode uses ACS PSFs and calibrations, and is effectively an ACS adaptation of HSTphot. A number of utility programs are also included with the DOLPHOT distribution, including basic image reduction routines.

[ascl:1709.004]
DOOp: DAOSPEC Output Optimizer pipeline

Cantat-Gaudin, Tristan; Donati, Paolo; Pancino, Elena; Bragaglia, Angela; Vallenari, Antonella; Friel, Eileen D.; Sordo, Rosanna; Jacobson, Heather R.; Magrini, Laura

The DAOSPEC Output Optimizer pipeline (DOOp) runs efficient and convenient equivalent widths measurements in batches of hundreds of spectra. It uses a series of BASH scripts to work as a wrapper for the FORTRAN code DAOSPEC (ascl:1011.002) and uses IRAF (ascl:9911.002) to automatically fix some of the parameters that are usually set by hand when using DAOSPEC. This allows batch-processing of quantities of spectra that would be impossible to deal with by hand. DOOp was originally built for the large quantity of UVES and GIRAFFE spectra produced by the Gaia-ESO Survey, but just like DAOSPEC, it can be used on any high resolution and high signal-to-noise ratio spectrum binned on a linear wavelength scale.

[ascl:1206.011]
Double Eclipsing Binary Fitting

The parameters of the mutual orbit of eclipsing binaries that are physically connected can be obtained by precision timing of minima over time through light travel time effect, apsidal motion or orbital precession. This, however, requires joint analysis of data from different sources obtained through various techniques and with insufficiently quantified uncertainties. In particular, photometric uncertainties are often underestimated, which yields too small uncertainties in minima timings if determined through analysis of a χ2 surface. The task is even more difficult for double eclipsing binaries, especially those with periods close to a resonance such as CzeV344, where minima get often blended with each other.

This code solves the double binary parameters simultaneously and then uses these parameters to determine minima timings (or more specifically O-C values) for individual datasets. In both cases, the uncertainties (or more precisely confidence intervals) are determined through bootstrap resampling of the original data. This procedure to a large extent alleviates the common problem with underestimated photometric uncertainties and provides a check on possible degeneracies in the parameters and the stability of the results. While there are shortcomings to this method as well when compared to Markov Chain Monte Carlo methods, the ease of the implementation of bootstrapping is a significant advantage.

[ascl:1504.012]
DPI: Symplectic mapping for binary star systems for the Mercury software package

DPI is a FORTRAN77 library that supplies the symplectic mapping method for binary star systems for the Mercury N-Body software package (ascl:1201.008). The binary symplectic mapping is implemented as a hybrid symplectic method that allows close encounters and collisions between massive bodies and is therefore suitable for planetary accretion simulations.

[ascl:1804.003]
DPPP: Default Pre-Processing Pipeline

DPPP (Default Pre-Processing Pipeline, also referred to as NDPPP) reads and writes radio-interferometric data in the form of Measurement Sets, mainly those that are created by the LOFAR telescope. It goes through visibilities in time order and contains standard operations like averaging, phase-shifting and flagging bad stations. Between the steps in a pipeline, the data is not written to disk, making this tool suitable for operations where I/O dominates. More advanced procedures such as gain calibration are also included. Other computing steps can be provided by loading a shared library; currently supported external steps are the AOFlagger (ascl:1010.017) and a bridge that enables loading python steps.

[ascl:1303.025]
DPUSER: Interactive language for image analysis

DPUSER is an interactive language capable of handling numbers (both real and complex), strings, and matrices. Its main aim is to do astronomical image analysis, for which it provides a comprehensive set of functions, but it can also be used for many other applications.

[ascl:1712.005]
draco: Analysis and simulation of drift scan radio data

draco analyzes transit radio data with the m-mode formalism. It is telescope agnostic, and is used as part of the analysis and simulation pipeline for the CHIME (Canadian Hydrogen Intensity Mapping Experiment) telescope. It can simulate time stream data from maps of the sky (using the m-mode formalism) and add gain fluctuations and correctly correlated instrumental noise (i.e. Wishart distributed). Further, it can perform various cuts on the data and make maps of the sky from data using the m-mode formalism.

[ascl:1512.009]
DRACULA: Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy

Aguena, Michel; Busti, Vinicius C.; Camacho, Hugo; Sasdelli, Michele; Ishida, Emille E. O.; Vilalta, Ricardo; Trindade, Arlindo M. M.; Gieseke, Fabien; de Souza, Rafael S.; Fantaye, Yabebal T.; Mazzali, Paolo A.

DRACULA classifies objects using dimensionality reduction and clustering. The code has an easy interface and can be applied to separate several types of objects. It is based on tools developed in scikit-learn, with some usage requiring also the H2O package.

[ascl:1106.011]
DRAGON: Galactic Cosmic Ray Diffusion Code

DRAGON adopts a second-order Cranck-Nicholson scheme with Operator Splitting and time overrelaxation to solve the diffusion equation. This provides a fast solution that is accurate enough for the average user. Occasionally, users may want to have very accurate solutions to their problem. To enable this feature, users may get close to the accurate solution by using the fast method, and then switch to a more accurate solution scheme featuring the Alternating-Direction-Implicit (ADI) Cranck-Nicholson scheme.

[ascl:1011.009]
DRAGON: Monte Carlo Generator of Particle Production from a Fragmented Fireball in Ultrarelativistic Nuclear Collisions

A Monte Carlo generator of the final state of hadrons emitted from an ultrarelativistic nuclear collision is introduced. An important feature of the generator is a possible fragmentation of the fireball and emission of the hadrons from fragments. Phase space distribution of the fragments is based on the blast wave model extended to azimuthally non-symmetric fireballs. Parameters of the model can be tuned and this allows to generate final states from various kinds of fireballs. A facultative output in the OSCAR1999A format allows for a comprehensive analysis of phase-space distributions and/or use as an input for an afterburner. DRAGON's purpose is to produce artificial data sets which resemble those coming from real nuclear collisions provided fragmentation occurs at hadronisation and hadrons are emitted from fragments without any further scattering. Its name, DRAGON, stands for DRoplet and hAdron GeneratOr for Nuclear collisions. In a way, the model is similar to THERMINATOR, with the crucial difference that emission from fragments is included.

[ascl:1811.002]
DRAGONS: Gemini Observatory data reduction platform

DRAGONS (Data Reduction for Astronomy from Gemini Observatory North and South) is Gemini's Python-based data reduction platform. DRAGONS offers an automation system that allows for hands-off pipeline reduction of Gemini data, or of any other astronomical data once configured. The platform also allows researchers to control input parameters and in some cases will offer to interactively optimize some data reduction steps, e.g. change the order of fit and visualize the new solution.

[ascl:1507.012]
DRAMA: Instrumentation software environment

DRAMA is a fast, distributed environment for writing instrumentation control systems. It allows low level instrumentation software to be controlled from user interfaces running on UNIX, MS Windows or VMS machines in a consistent manner. Such instrumentation tasks can run either on these machines or on real time systems such as VxWorks. DRAMA uses techniques developed by the AAO while using the Starlink-ADAM environment, but is optimized for the requirements of instrumentation control, portability, embedded systems and speed. A special program is provided which allows seamless communication between ADAM and DRAMA tasks.

[ascl:1504.006]
drive-casa: Python interface for CASA scripting

drive-casa provides a Python interface for scripting of CASA (ascl:1107.013) subroutines from a separate Python process, allowing for utilization alongside other Python packages which may not easily be installed into the CASA environment. This is particularly useful for embedding use of CASA subroutines within a larger pipeline. drive-casa runs plain-text casapy scripts directly; alternatively, the package includes a set of convenience routines which try to adhere to a consistent style and make it easy to chain together successive CASA reduction commands to generate a command-script programmatically.

[ascl:1212.011]
DrizzlePac: HST image software

DrizzlePac allows users to easily and accurately align and combine HST images taken at multiple epochs, and even with different instruments. It is a suite of supporting tasks for AstroDrizzle which includes:

- astrodrizzle to align and combine images
- tweakreg and tweakback for aligning images in different visits
- pixtopix transforms an X,Y pixel position to its pixel position after distortion corrections
- skytopix transforms sky coordinates to X,Y pixel positions. A reverse transformation can be done using the task pixtosky.

[ascl:1610.003]
DSDEPROJ: Direct Spectral Deprojection

Deprojection of X-ray data by methods such as PROJCT, which are model dependent, can produce large and unphysical oscillating temperature profiles. Direct Spectral Deprojection (DSDEPROJ) solves some of the issues inherent to model-dependent deprojection routines. DSDEPROJ is a model-independent approach, assuming only spherical symmetry, which subtracts projected spectra from each successive annulus to produce a set of deprojected spectra.

[ascl:1010.006]
DSPSR: Digital Signal Processing Software for Pulsar Astronomy

DSPSR, written primarily in C++, is an open-source, object-oriented, digital signal processing software library and application suite for use in radio pulsar astronomy. The library implements an extensive range of modular algorithms for use in coherent dedispersion, filterbank formation, pulse folding, and other tasks. The software is installed and compiled using the standard GNU configure and make system, and is able to read astronomical data in 18 different file formats, including FITS, S2, CPSR, CPSR2, PuMa, PuMa2, WAPP, ASP, and Mark5.

[ascl:1501.004]
dst: Polarimeter data destriper

Dst is a fully parallel Python destriping code for polarimeter data; destriping is a well-established technique for removing low-frequency correlated noise from Cosmic Microwave Background (CMB) survey data. The software destripes correctly formatted HDF5 datasets and outputs hitmaps, binned maps, destriped maps and baseline arrays.

[ascl:1505.034]
dStar: Neutron star thermal evolution code

dStar is a collection of modules for computing neutron star structure and evolution, and uses the numerical, utility, and equation of state libraries of MESA (ascl:1010.083).

[ascl:1201.011]
Duchamp: A 3D source finder for spectral-line data

Duchamp is software designed to find and describe sources in 3-dimensional, spectral-line data cubes. Duchamp has been developed with HI (neutral hydrogen) observations in mind, but is widely applicable to many types of astronomical images. It features efficient source detection and handling methods, noise suppression via smoothing or multi-resolution wavelet reconstruction, and a range of graphical and text-based outputs to allow the user to understand the detections.

[ascl:1605.014]
DUO: Spectra of diatomic molecules

Duo computes rotational, rovibrational and rovibronic spectra of diatomic molecules. The software, written in Fortran 2003, solves the Schrödinger equation for the motion of the nuclei for the simple case of uncoupled, isolated electronic states and also for the general case of an arbitrary number and type of couplings between electronic states. Possible couplings include spin–orbit, angular momenta, spin-rotational and spin–spin. Introducing the relevant couplings using so-called Born–Oppenheimer breakdown curves can correct non-adiabatic effects.

[ascl:1503.005]
dust: Dust scattering and extinction in the X-ray

Written in Python, dust calculates X-ray dust scattering and extinction in the intergalactic and local interstellar media.

[ascl:1908.016]
DustCharge: Charge distribution for a dust grain

Ibáñez-Mejía, Juan C.; Walch, Stefanie; Ivlev, Alexei V.; Clarke, Seamus; Caselli, Paola; Joshi, Prabesh R.

DustCharge calculates the equilibrium charge distribution for a dust grain of a given size and composition, depending on the local interstellar medium conditions, such as density, temperature, ionization fraction, local radiation field strength, and cosmic ray ionization fraction.

[ascl:1307.001]
DustEM: Dust extinction and emission modelling

Compiègne, M.; Verstraete, L.; Jones, A.; Bernard, J.-P.; Boulanger, F.; Flagey, N.; Le Bourlot, J.; Paradis, D.; Ysard, N.

DustEM computes the extinction and the emission of interstellar dust grains heated by photons. It is written in Fortran 95 and is jointly developed by IAS and CESR. The dust emission is calculated in the optically thin limit (no radiative transfer) and the default spectral range is 40 to 108 nm. The code is designed so dust properties can easily be changed and mixed and to allow for the inclusion of new grain physics.

[ascl:9911.001]
DUSTY: Radiation transport in a dusty environment

DUSTY solves the problem of radiation transport in a dusty environment. The code can handle both spherical and planar geometries. The user specifies the properties of the radiation source and dusty region, and the code calculates the dust temperature distribution and the radiation field in it. The solution method is based on a self-consistent equation for the radiative energy density, including dust scattering, absorption and emission, and does not introduce any approximations. The solution is exact to within the specified numerical accuracy. DUSTY has built in optical properties for the most common types of astronomical dust and comes with a library for many other grains. It supports various analytical forms for the density distribution, and can perform a full dynamical calculation for radiatively driven winds around AGB stars. The spectral energy distribution of the source can be specified analytically as either Planckian or broken power-law. In addition, arbitrary dust optical properties, density distributions and external radiation can be entered in user supplied files. Furthermore, the wavelength grid can be modified to accommodate spectral features. A single DUSTY run can process an unlimited number of models, with each input set producing a run of optical depths, as specified. The user controls the detail level of the output, which can include both spectral and imaging properties as well as other quantities of interest.

[ascl:1602.004]
DUSTYWAVE: Linear waves in gas and dust

Written in Fortran, DUSTYWAVE computes the exact solution for linear waves in a two-fluid mixture of gas and dust. The solutions are general with respect to both the dust-to-gas ratio and the amplitude of the drag coefficient.

[ascl:1809.013]
dynesty: Dynamic Nested Sampling package

dynesty is a Dynamic Nested Sampling package for estimating Bayesian posteriors and evidences. dynesty samples from a given distribution when provided with a loglikelihood function, a prior_transform function (that transforms samples from the unit cube to the target prior), and the dimensionality of the parameter space.

[ascl:1902.010]
dyPolyChord: Super fast dynamic nested sampling with PolyChord

dyPolyChord implements dynamic nested sampling using the efficient PolyChord (ascl:1502.011) sampler to provide state-of-the-art nested sampling performance. Any likelihoods and priors which work with PolyChord can be used (Python, C++ or Fortran), and the output files produced are in the PolyChord format.

[ascl:1407.017]
e-MERLIN data reduction pipeline

Written in Python and utilizing ParselTongue (ascl:1208.020) to interface with AIPS (ascl:9911.003), the e-MERLIN data reduction pipeline processes, calibrates and images data from the UK's radio interferometric array (Multi-Element Remote-Linked Interferometer Network). Driven by a plain text input file, the pipeline is modular and can be run in stages. The software includes options to load raw data, average in time and/or frequency, flag known sources of interference, flag more comprehensively with SERPent (ascl:1312.001), carry out some or all of the calibration procedures (including self-calibration), and image in either normal or wide-field mode. It also optionally produces a number of useful diagnostic plots at various stages so data quality can be assessed.

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